Atividade Final

Informações gerais

Esse banco de dados é de um resultado de um projeto de Ciência cidadã feito em Arraial do Cabo. A fotos recebidas foram utilizadas para identificação individual das tartarugas marinhas através do método de foto-identificação. O software utilizado para as comparações foi o Hotspotter, com intermédio do Internet of Turtles. Os dados podem ser encontrados no Internet of Turtles procurando pelo ID “isabellaferreira”. Para esse trabalho foram extraídos os encontros (avistamento de um único animal em um local e horário específicos) registrados até a data atual.

• Name0.value: Identidade única para cada indivíduo
• Occurrence.occurrenceID: Código único para cada encontro
• Encounter.verbatimLocality: Local de coleta da foto, se NA, então a foto é de Arraial do Cabo sem definição da praia ou ponto de mergulho
• Encounter.year: Ano da coleta da foto, formato de quatro dígitos
• Encounter.month: Mês da coleta da foto, formato de dois dígitos
• Encounter.day: Dia da coleta da foto, formato de dois dígitos.Dia 0 indica fotos sem dia definido.
• Encounter.behavior: Comportamento do animal no momento do encontro
• Encounter.genus: Gênero da espécie do encontro
• Encounter.specificEpithet: Epíteto específico da espécie do encontro
• Encounter.occurrenceRemarks: Lateral(is) registrada(s) no encontro, podendo ser direita (right), esquerda (left), ou ambas as laterais (both)
• Encounter.mediaAsset0: Primeira foto do encontro, podendo ser da lateral direita ou esqueda. Obrigatória para registro do encontro.
• Encounter.mediaAsset1: Segunda foto do encontro, sendo necessária apenas quando o encontro tem registro das duas laterais.

Pacotes usados

Carregando os pacotes

library(readxl)

## Warning: package 'readxl' was built under R version 4.0.5

library(tidyverse)

## Warning: package 'tidyverse' was built under R version 4.0.5

## Warning: package 'ggplot2' was built under R version 4.0.5

## Warning: package 'tibble' was built under R version 4.0.5

## Warning: package 'tidyr' was built under R version 4.0.5

## Warning: package 'readr' was built under R version 4.0.5

## Warning: package 'purrr' was built under R version 4.0.5

## Warning: package 'dplyr' was built under R version 4.0.5

## Warning: package 'stringr' was built under R version 4.0.5

## Warning: package 'forcats' was built under R version 4.0.5

library(stringr)
library(psych)

## Warning: package 'psych' was built under R version 4.0.5

library(lubridate)

## Warning: package 'lubridate' was built under R version 4.0.5

library(plyr)

## Warning: package 'plyr' was built under R version 4.0.5

library(plotly)

## Warning: package 'plotly' was built under R version 4.0.5

Analisando o total de indivíduos

Abrindo a tabela e selecionando as colunas

originalenctable <- read_excel("encounterSearchResults_export_isabellaferreira.xls") %>%
  select(Name0.value, Occurrence.occurrenceID, Encounter.verbatimLocality,Encounter.year,Encounter.month,Encounter.day, Encounter.behavior,Encounter.genus,Encounter.specificEpithet,Encounter.occurrenceRemarks,Encounter.mediaAsset0,Encounter.mediaAsset1)

Analisando a tabela

lapply(originalenctable, unique)

## $Name0.value
##   [1] "MdT344"     "MdT316"     "MdT6"       "MdT342"     "MdT72"     
##   [6] "MdT343"     "MdT341"     "MdT314"     "MdT345"     "MdT311"    
##  [11] "MdT312"     "MdT310"     "MdT313"     "MdT303"     "MdT304"    
##  [16] "MdT294"     "Bicuda"     "MdT308"     "MdT307"     "MdT295"    
##  [21] "MdT296"     "MdT293"     "MdT292"     "MdT291"     "MdT300"    
##  [26] "MdT290"     "MdT289"     "MdT299"     "MdT288"     "MdT287"    
##  [31] "MdT285"     "MdT286"     "MdT346"     "MdT330"     "MdT326"    
##  [36] "MdT325"     "MdT327"     "MdT328"     "MdT329"     "MdT302"    
##  [41] "MdT320"     "MdT324"     "MdT322"     "MdT323"     "MdT321"    
##  [46] "MdT319"     "MdT261"     "MdT335"     "MdT333"     "MdT78"     
##  [51] "MdT339"     "MdT33"      "MdT340"     "MdT253"     "MdT331"    
##  [56] "MdT334"     "MdT338"     "MdT336"     "MdT348"     "MdT262"    
##  [61] "MdT259"     "MdT337"     "MdT309"     "MdT305"     "MdT306"    
##  [66] "MdT284"     "MdT280"     "MdT317"     "MdT315"     "MdT282"    
##  [71] "MdT283"     "MdT318"     "MdT277"     "MdT276"     "MdT275"    
##  [76] "MdT278"     "MdT271"     "MdT272"     "MdT273"     "MdT274"    
##  [81] "MdT279"     "MdT270"     "MdT269"     "MdT268"     "MdT298"    
##  [86] "MdT266"     "MdT265"     "MdT267"     "MdT264"     "MdT263"    
##  [91] "MdT75"      "Rachel"     "MdT251"     "MdT250"     "MdT24"     
##  [96] "MdT86"      "MdT249"     "MdT252"     "MdT260"     "MdT256"    
## [101] "MdT254"     "MdT257"     "MdT255"     "MdT258"     "MdT206"    
## [106] "MdT243"     "MdT237"     "MdT159"     "MdT105"     "MdT83"     
## [111] "MdT28"      "MdT244"     "MdT126"     "MdT248"     "MdT229"    
## [116] "MdT224"     "MdT100"     "Fafa"       "MdT54"      "MdT221"    
## [121] "MdT222"     "MdT226"     "MdT84"      "MdT236"     "MdT179"    
## [126] "MdT146"     "Margarida"  "Tikinha"    "Dorminhoca" "MdT234"    
## [131] "MdT183"     "MdT150"     "MdT87"      "MdT90"      "MdT246"    
## [136] "MdT141"     "MdT135"     "MdT124"     "MdT68"      "Drika"     
## [141] "MdT136"     "MdT127"     "MdT191"     "MdT155"     "MdT40"     
## [146] "MdT202"     "MdT149"     "MdT162"     "MdT77"      "MdT138"    
## [151] "MdT74"      "MdT82"      "MdT140"     "MdT184"     "MdT185"    
## [156] "MdT247"     "MdT200"     "MdT80"      "Judite"     "MdT134"    
## [161] "MdT347"     "MdT301"     "MdT148"     "MdT115"     "MdT139"    
## [166] "MdT73"      "MdT114"     "MdT192"     "MdT130"     "MdT228"    
## [171] "MdT175"     "MdT76"      "MdT238"     "MdT147"     "MdT142"    
## [176] "MdT132"     "MdT106"     "MdT39"      "MdT89"      "MdT181"    
## [181] "MdT182"     "MdT92"      "MdT217"     "MdT97"      "MdT117"    
## [186] "MdT31"      "MdT52"      "MdT55"      "Josimar"    "MdT133"    
## [191] "MdT194"     "MdT193"     "MdT204"     "MdT203"     "MdT36"     
## [196] "MdT225"     "MdT154"     "MdT165"     "MdT50"      "MdT196"    
## [201] "MdT241"     "MdT239"     "MdT79"      "MdT151"     "MdT164"    
## [206] "MdT161"     "MdT102"     "MdT101"     "MdT93"      "MdT190"    
## [211] "MdT49"      "MdT96"      "MdT110"     "MdT176"     "MdT48"     
## [216] "MdT116"     "MdT240"     "MdT98"      "MdT88"      "MdT111"    
## [221] "MdT137"     "MdT107"     "Brava"      "MdT177"     "MdT157"    
## [226] "MdT156"     "MdT220"     "MdT119"     "MdT47"      "MdT218"    
## [231] "MdT208"     "MdT212"     "MdT143"     "MdT216"     "MdT118"    
## [236] "MdT153"     "MdT213"     "MdT103"     "MdT211"     "MdT209"    
## [241] "MdT219"     "MdT201"     "MdT95"      "MdT35"      "MdT104"    
## [246] "MdT170"     "MdT123"     "MdT46"      "MdT125"     "MdT172"    
## [251] "MdT169"     "MdT71"      "MdT242"     "MdT160"     "MdT158"    
## [256] "MdT199"     "MdT45"      "MdT163"     "MdT43"      "MdT42"     
## [261] "MdT41"      "MdT231"     "MdT109"     "MdT94"      "MdT128"    
## [266] "MdT205"     "MdT232"     "MdT214"     "MdT230"     "MdT112"    
## [271] "MdT227"     "MdT233"     "MdT197"     "MdT129"     "MdT166"    
## [276] "MdT245"     "MdT207"     "MdT38"      "MdT198"     "MdT186"    
## [281] "MdT62"      "MdT37"      "MdT34"      "MdT32"      "MdT187"    
## [286] "MdT30"      "MdT29"      "MdT131"     "MdT180"     "MdT67"     
## [291] "MdT66"      "MdT27"      "MdT26"      "MdT64"      "MdT168"    
## [296] "MdT25"      "MdT63"      "MdT69"      "MdT70"      "MdT61"     
## [301] "MdT22"      "MdT23"      "MdT21"      "Aninha"     "MdT235"    
## [306] "MdT20"      "MdT19"      "MdT18"      "MdT58"      "MdT59"     
## [311] "MdT60"      "MdT17"      "MdT15"      "MdT16"      "MdT13"     
## [316] "MdT14"      "MdT12"      "MdT11"      "MdT57"      "MdT10"     
## [321] "MdT8"       "MdT9"       "MdT7"       "MdT5"       "MdT3"      
## [326] "MdT56"      "MdT4"       "MdT167"     "MdT2"       "MdT1"      
## [331] "MdT53"     
## 
## $Occurrence.occurrenceID
##   [1] "973de39d-ae5e-42ed-88d0-c434b407c08e"
##   [2] "9ef424bc-29e9-4cbf-8694-40e8475860fb"
##   [3] "e271bbfa-5602-444a-9a93-24638faedb88"
##   [4] "351253c5-4502-4de2-9116-64be4880be19"
##   [5] "7ffdc664-33b3-44b3-84e7-80a72577f956"
##   [6] "74f7ef96-c924-466e-adb0-95444c301cfe"
##   [7] "3402c99a-1ca8-43f3-b404-273425b539b0"
##   [8] "ecf6ee74-3a23-4f4c-be1e-ec7d6f5798ef"
##   [9] "0218cd5f-0fee-4ec3-b86f-73f4078f4e0e"
##  [10] "4e0e877c-9205-4e61-826c-5adddd562280"
##  [11] "930843de-ee99-4917-8bf6-53e81a68b0bf"
##  [12] "cba1cd3b-337b-4af6-a466-12c0b5f1df9e"
##  [13] "ddb8d6ad-48a5-4bac-adf2-143bccac97d6"
##  [14] "6e1e8f49-877b-471f-8c3c-939d6d1c5584"
##  [15] "ed790f9c-26bf-4f5d-911e-bfe825fa6b76"
##  [16] "3475a627-73a9-40a3-b6c2-a634e2ca8e5a"
##  [17] "f6e0e172-3b6e-405e-a99f-2d8e94d1c916"
##  [18] "578d9740-246f-402a-8c81-b06c0157623a"
##  [19] "853854a5-3f0a-423e-9e83-f56854006889"
##  [20] "d6274bf0-759e-410c-98e9-739ce325b558"
##  [21] "2b3bc78b-8b35-4820-8e29-cadde81b7c5d"
##  [22] "9c9b8ef7-01fb-4901-9699-ddbe69d52441"
##  [23] "485a0f19-b8da-4a77-9b51-74a415c88339"
##  [24] "ee62b8b0-97b4-417a-84a1-70ccbd3eed5c"
##  [25] "239c7e90-8a30-4671-b986-85803a3de6d0"
##  [26] "76f19270-931b-46bd-a8bb-8a76859787a3"
##  [27] "72248b37-03ef-40cb-bbf7-849bdaca7c0f"
##  [28] "497fa259-ad2f-4458-8b8e-37d1c59923b1"
##  [29] "3db0f325-5e9b-4a5d-9dd0-8296d8226b6e"
##  [30] "f2c398ba-d5f9-441d-9842-ab39b0414106"
##  [31] "bd39585e-eb8b-4fd3-81c8-0b81aadedddf"
##  [32] "54dd65db-5a5e-4e1f-97d9-477c253443d4"
##  [33] "10a43c8d-d344-456b-b5f1-1693fc651d26"
##  [34] "62224628-7681-4166-8327-999229bdd02e"
##  [35] "73deb8a0-be46-4741-aa04-fe2aee2d79e4"
##  [36] "c118013f-ec09-4a96-a180-e87bf3e252c3"
##  [37] "07ced264-39d9-4b60-87d5-0a7266fc3cf1"
##  [38] "4d37bd87-7d78-4e0e-aa7f-343f83ab215d"
##  [39] "549285f8-0c1d-4f25-a042-e0c96e9ba793"
##  [40] "1457fc4d-eacf-4d01-bddc-ad455d13ce66"
##  [41] "66b7752a-9da7-454d-a222-d564926020b4"
##  [42] "b461abcf-6b4f-4f51-9900-e0ef64c85c05"
##  [43] "9b2225ed-44cc-4ff1-8de1-b6a3a887b95d"
##  [44] "ea9e1c60-2e4a-41b9-830d-b7080ba40725"
##  [45] "bd44ee8e-cb99-49aa-9237-7c1fd9ab7c46"
##  [46] "b53b58c3-be01-4554-8f98-219440bb8bb7"
##  [47] "39e926d9-29fe-4c92-8222-caac4ee4b075"
##  [48] "6d5b3e67-1ada-41c9-a401-21ad61ff1b03"
##  [49] "7d722e89-d9ac-49cd-8b15-23202ba79a2b"
##  [50] "188a512c-45ab-4280-a7e5-f8a1a6a5b0ab"
##  [51] "c22c7d9d-8f98-4fa5-910e-9cc1db240042"
##  [52] "595c88ae-a00c-4c8a-907f-fd84e6a3f75e"
##  [53] "660f911c-c9a9-410b-b94f-0352ea816ffd"
##  [54] "76b64337-1695-4834-a947-9d165ddf9188"
##  [55] "76e33fbe-e990-4b38-8a71-f96781408019"
##  [56] "7b29ddc3-80b9-463d-9473-b4062508f6f2"
##  [57] "4dd55dad-dba1-4b72-a616-aab131f4c8ee"
##  [58] "a13339eb-4a1f-4ac4-9fa3-54e98e5c24f1"
##  [59] "a671973a-eb6c-43b8-9f8c-e79017aae71d"
##  [60] "a243d263-a1ad-4fb1-a869-abf1bb8dd2b9"
##  [61] "826d424b-10b5-4de9-9e79-6347cf777962"
##  [62] "a67cc0ff-b75a-4abe-86c4-1e8376fabe77"
##  [63] "c9388721-d0ac-4448-929f-211ab9221d6d"
##  [64] "6666d2ea-023b-407d-a2af-2c7f65887aea"
##  [65] "14aeb1a1-f127-42b2-9420-bc2ad51adadc"
##  [66] "4b23bd80-2b61-47bd-8f9f-d119c9947592"
##  [67] "715271aa-c177-4fec-882f-5b97508e1685"
##  [68] "5dcb1370-aa23-4fb8-98b6-9f378472fa65"
##  [69] "c1cbce80-213f-4e76-8a79-096f1b61865a"
##  [70] "07013054-9c63-4f1b-bbc1-e0088f00869c"
##  [71] "a9112bb1-40bf-445d-9890-0d6347c89d88"
##  [72] "be15d993-13fd-496c-a209-13720b9e4c66"
##  [73] "e443b793-8fd2-4d16-9cdc-7eee8b093dc5"
##  [74] "21568d1d-096e-4eaf-8d3e-3507ec6927e5"
##  [75] "21138f85-b905-452e-8751-f32854c4687f"
##  [76] "247bde9a-028f-4f44-8b3a-dcd036469042"
##  [77] "173deb25-eb90-421f-aaae-36bb1028f83b"
##  [78] "b57c77b6-fe90-4743-a19c-afe2a300b29f"
##  [79] "0e685b11-b20a-4697-94da-969d66d0fca1"
##  [80] "850fb78b-3685-4d17-aabb-60f6cce9e6b8"
##  [81] "c04cddaf-958a-49d4-aea0-1ae12bd1b5ae"
##  [82] "7e5884f0-8501-41bd-a5a6-cda47b731b44"
##  [83] "c21e1445-963d-46f0-b356-2b09baaf5ee3"
##  [84] "8f8c9319-0bd4-4f53-ac63-80f156d3e255"
##  [85] "ff73ae43-1c24-46fa-ab6a-e92e6815e392"
##  [86] "ce523310-dc44-4e76-93ea-b1301186dbeb"
##  [87] "2def5073-227a-461e-bcaa-9ee17b968118"
##  [88] "1e05c12c-15e8-4141-a08f-e2235fbecead"
##  [89] "e93523c3-f7c7-4dfc-a391-6ef68fe92043"
##  [90] "d860e665-d8fe-4a7a-9eef-922d9c0bae8d"
##  [91] "eed1958f-7910-4b0d-b18b-1069eabf72fd"
##  [92] "3416e758-5569-4ee4-a0a1-9b74ce14ae13"
##  [93] "7c415ee8-a947-44ad-a3c0-3d698816e4cd"
##  [94] "63764a2d-c968-4054-9f70-328d0ce63be8"
##  [95] "53eddd04-8641-4588-bd8d-1c559c734214"
##  [96] "712a6a51-545b-434f-8b64-57f43c78b6bd"
##  [97] "21da0257-d661-4cc3-8145-b8fbced6ceaa"
##  [98] "13d8693e-741f-4d36-8b2d-c8858109bfdb"
##  [99] "9349c548-0a7c-4c43-994f-3237a8c0e51a"
## [100] "5ac4a655-d4a9-4dbe-adc1-34f2249a8036"
## [101] "98e38830-04f7-4174-af8d-11f4abfd227d"
## [102] "422e8191-0cff-4f3d-9de4-2f55ea421624"
## [103] "47fd07a2-f786-4402-b65c-844ae8215cd8"
## [104] "400b9db2-cfb6-45fa-96f7-61c54f466e3c"
## [105] "9613d0dc-947a-4126-b685-7d371c921bfa"
## [106] "476ffa1c-0e56-42bf-978c-2cb2897574f6"
## [107] "1f84535b-8b71-4111-b24e-d103d74c6dcf"
## [108] "bf6f19a8-c476-4319-ad7d-ffddaf1f9793"
## [109] "f2ce2535-5fb1-4fab-b92b-dcf9a7140d26"
## [110] "9f230287-b2d1-44b4-9507-3e05a2c6b503"
## [111] "c8101b64-16a4-4d03-b332-0b73fbbb6068"
## [112] "42802e76-08ee-4bca-8daa-5232434d36b2"
## [113] "8f4865b5-fab4-4c8e-91b6-325c6c991a37"
## [114] "d4d8aacf-6e7c-4c37-bae8-1e4fabdff478"
## [115] "f2458ea3-9026-4cca-8e7d-4ee2dbc52e5d"
## [116] "1b33a653-9f48-4964-9b0b-1917ad6f68bb"
## [117] "5e9921d4-d973-4ba5-924b-a204ee581174"
## [118] "fae69a0d-3eb6-416a-9224-25f27d69f8e1"
## [119] "1d5da146-5bb0-45b6-9d12-aca2b8525ee1"
## [120] "90526516-26c5-42f6-b4c0-a99b92871bce"
## [121] "92c5007f-c145-4681-a533-ca6c772293cd"
## [122] "d55e9d0f-38db-4a21-853c-382c863e7c58"
## [123] "88a0c16d-325f-4393-977a-bd3b26709267"
## [124] "d2fc7511-b222-4e48-9acb-57f2e71537e5"
## [125] "5d8331b2-6804-4aeb-a367-961cf4b7fa3c"
## [126] "fce9711d-3bc0-40d2-ae86-618ea0d2aaa8"
## [127] "9e0daef8-6e0d-4f54-8f76-f23512646ce8"
## [128] "704b09b5-56e6-4342-80a0-8a03fbf7b28f"
## [129] "16a54671-9569-42af-ba0d-9b84dc7b129e"
## [130] "e7200334-2f5d-4275-b09a-2c72e4e43714"
## [131] "490a2ec4-1d69-454c-9d52-0dd49e3d9c15"
## [132] "9774c172-d64e-4a35-8d1e-8bf72c0a8c8e"
## [133] "8bf59c5c-18c4-4e3a-b644-f7a8d46fb830"
## [134] "472eb9ab-3bb3-401b-99d7-647873ec01f1"
## [135] "434b64c3-f1ae-46ea-b1b1-2beec78525b9"
## [136] "5849e726-a300-4421-bdc9-970077533ae3"
## [137] "703d6bb8-94f6-4bec-a2e6-a97a1ad28348"
## [138] "0ee085cb-32b8-4915-bcf5-72e16c62c2ed"
## [139] "8fec92f8-6e9b-421c-8ee8-5d1329803d15"
## [140] "aa5c5629-8262-4c84-a490-f1e4daa2907e"
## [141] "ff212e81-a589-4f97-8b51-9c72cb5fbc9b"
## [142] "34efeacb-0c7c-4d68-9ad7-20f7fa1d1fcd"
## [143] "f6025c1d-c6b8-4258-a361-3e0b7323e588"
## [144] "0ee850c5-6fe2-4bc2-a796-6a3b3d273d77"
## [145] "e7db63fc-1ac7-4968-8f9d-b43a267b66be"
## [146] "3ec31280-541f-47d7-82e0-d403c779046e"
## [147] "3587febe-7d11-45e9-930f-cdf23fa8075b"
## [148] "6ddd4296-5326-47a2-98a8-d04ce0aa8432"
## [149] "74a094ce-ecda-4a2d-a36c-33368c2cc8e9"
## [150] "bfe3cc4e-8d06-4aa0-bb69-a48a2d663ab0"
## [151] "5ded0be4-b79e-45d4-a18f-5a0ebfd348a9"
## [152] "97685e2a-990b-4bab-b510-5505dfd728f8"
## [153] "83bfbd9b-cc4b-409e-af1a-fbd318c2d6ca"
## [154] "e1a09b84-5373-4a9b-ab6f-de6dbfed1bed"
## [155] "4a07c062-16fe-42be-ac54-a7605e7e9fcd"
## [156] "62d48058-5e68-4448-af0f-8de55d14aa2c"
## [157] "62ef7f87-6942-439d-b406-255bf5622cfb"
## [158] "10461e35-40e6-486d-be00-f93fbfb45195"
## [159] "df541e7e-09b7-4e1f-9071-cd87cec766ff"
## [160] "2b183df1-3c49-4b56-8e5f-fe51f40f97d8"
## [161] "5c0be005-d862-477a-a7ec-40af8bf0f1f0"
## [162] "97891d67-f66d-42e1-a6df-7e74b8964751"
## [163] "1c0c4845-ed16-41e6-8218-ae4fd95d79d0"
## [164] "5e8e8510-e4fd-4006-9a5f-4b227fc1aa9a"
## [165] "18354111-bb2d-492c-9540-eb2ad96571eb"
## [166] "951619c1-cc01-4868-adb4-17a435ae4b24"
## [167] "01abde41-104e-46dd-8ffb-2cf2a1e14a9b"
## [168] "dc8ce722-3410-4628-9d5e-43c1cf15008f"
## [169] "22339789-4f4f-4271-9343-e11c50cb3b4f"
## [170] "2c9b0f5e-7251-4d57-9a28-056fbccf53a2"
## [171] "b5010f38-3e52-4ba7-9b3f-368ecde5cee9"
## [172] "c9a96470-e86f-45d7-800c-5cefef027966"
## [173] "c72549d5-e596-4c75-bb7d-54af98d6e804"
## [174] "f7ecc2af-a690-4fd3-baf4-d10a455127f9"
## [175] "15fe9141-a34f-49cd-b74b-be2bbbf85e6a"
## [176] "eb09fcf7-d619-4c37-b55e-2419661f840c"
## [177] "8a6ef496-ec21-4d19-a9cd-e96b7a422c5d"
## [178] "37506637-14c6-47b4-abd9-459cff3054c0"
## [179] "c81c1a6c-333a-4791-9af6-1a2ebb80d466"
## [180] "3e9f753e-5a97-4eba-89fa-dc9a795a3a55"
## [181] "d20f8177-4e44-48b9-9a71-98296ead2088"
## [182] "ffa64434-6809-4d61-8fea-6a26076ca84a"
## [183] "a2d89ad8-2572-4c08-b246-6930646efa33"
## [184] "be8b9d37-cf51-425c-ab16-6eb9ae398943"
## [185] "171af485-e3f2-45e1-ab53-e439af2aee1f"
## [186] "68fc9fcb-c233-4413-ba1b-2d7b6c2a591f"
## [187] "057f7678-db2a-4f6e-9695-ab2de4ca6e44"
## [188] "de89e421-df32-4fa5-a975-2d7d3e744275"
## [189] "e5bdac29-b761-4ea7-a5d5-1fb22f1c96f4"
## [190] "dfaeb469-9f84-457c-b216-9f75b956de85"
## [191] "1869fd7f-443b-499c-a7d3-3f0b5d2d2314"
## [192] "c28e478b-2c6a-4c89-af68-9709f6d3cb5f"
## [193] "523959c0-0d38-44d4-89e4-8a6ffd647b07"
## [194] "aa38a8cf-274f-4f48-90ac-5f82e5f5be69"
## [195] "42b0aa8e-679a-44c5-9675-e9d788e5f7b7"
## [196] "d6a94149-60a5-4135-8e08-fe30ed75beeb"
## [197] "7f731302-cc26-4505-ae0b-e89afc685542"
## [198] "a484e6ab-bc39-4135-b40f-e642f5a671c1"
## [199] "a5e3bbf9-a4f4-4d07-9581-212f6bb08343"
## [200] "9887ae86-804c-4308-86c4-371187a4dca9"
## [201] "6000a0d7-5cfb-489a-8f04-ae496ff6bd1a"
## [202] "2ba12dcb-4629-48be-a2cb-52bffca92688"
## [203] "7140f941-03cb-4989-837f-171a6debc1ca"
## [204] "92ea6732-ecdc-4efd-b292-818ae57c0bbb"
## [205] "135865a2-bc18-462b-a65f-398c6b808e2c"
## [206] "55dc91d9-210e-489e-8cc7-1dc6481d36c7"
## [207] "54244306-f349-461a-b0fd-4a8e135ff318"
## [208] "4f5e3d67-9efd-42a9-a169-c9ce82d2fa8e"
## [209] "88032314-83d0-4fe9-ab42-d5fc56217c93"
## [210] "46ebd159-4494-4fee-8774-aa54d659f515"
## [211] "1f892d91-d517-4618-b19f-f749129df4b1"
## [212] "45dd0614-2df5-48cc-baff-7bdb1ef532c0"
## [213] "19c154b0-4fff-4838-9de5-8eb04946fcbc"
## [214] "0b0c751b-f4cf-41ad-9ea9-670885798468"
## [215] "9a54a0aa-b884-4ee7-b936-5796c15b4271"
## [216] "295d275a-df56-4515-b4e2-605316d9c83f"
## [217] "0edb403b-af80-4196-9db3-10f40164c984"
## [218] "0e357782-7e55-4b6c-b32d-736819c45f07"
## [219] "960a5844-76d9-4bb7-a993-8a3539dc2ab5"
## [220] "a2c66c70-68eb-4c80-b2d3-3b8ec2b46449"
## [221] "d1f128bf-3930-4463-81d0-256763195a81"
## [222] "fde6f535-acf1-4ac2-9118-ff8a9984d252"
## [223] "f9886bbc-8083-42dd-97e6-60f04e5df074"
## [224] "3b979d54-4c86-4ba3-a980-12926f34fa68"
## [225] "9c8c458b-be9c-4cfa-a4e7-565f05a22475"
## [226] "9b8eb5c4-fcbd-438b-830a-37743bd99715"
## [227] "265dd34c-54f3-4c11-bbf7-b5a71af4ff8a"
## [228] "0344eb09-40fd-4217-b734-77772103e286"
## [229] "5af519d1-07ce-4a26-9ddb-1abf1022fd37"
## [230] "2f1aed65-f197-439a-aa9c-e3831c91dea0"
## [231] "1aacfda7-3cf2-4f57-b8ed-0396dffa3092"
## [232] "f54c5eae-a4c9-4511-b5e6-61176946374c"
## [233] "b81676a3-1423-49d7-8fb2-a3b0786a5a34"
## [234] "b1686525-7b6c-465f-8521-efab419ed90a"
## [235] "c4208156-d50c-4e79-a66a-e0c786948637"
## [236] "fe37004e-50af-4f92-bde0-8fe76c58e9e2"
## [237] "0c8ce375-d483-4e0a-9850-e80283a003dc"
## [238] "2f13803d-3e51-4e04-9e53-8cd97ec91fdb"
## [239] "008aac3e-1e22-4885-ad7b-34994c8f7892"
## [240] "fe08ade6-9945-47b5-a046-b7f34f8ffe40"
## [241] "5f6ef5d6-2cd8-48f6-9a10-27e02796dc61"
## [242] "cadc0db5-9b64-4b64-a351-398f35bc5abf"
## [243] "3c11ec47-868c-4dd8-b164-896ce326c0ec"
## [244] "e8538ff8-853f-4cbc-ab4a-9c647e08ed57"
## [245] "346afad4-a8fa-4876-888a-ad85918c5ef9"
## [246] "b08dc085-1c87-4130-aa89-13153678cfea"
## [247] "a0e60e81-71bd-4509-a28c-1108502aef90"
## [248] "7254286e-3b22-4d1a-8887-fb31ce499d32"
## [249] "428770da-5d7e-4f65-9756-207f214dc997"
## [250] "72aafb7a-9aff-484e-9c3c-1bdf9169aefa"
## [251] "a7c07bba-fd43-40d0-a4f2-37faf022cfbe"
## [252] "45c3aabe-7366-4ff4-9f79-f47b20447d62"
## [253] "841f0447-6e68-4621-ab1b-517206b771e2"
## [254] "69b0add5-4861-4d10-8524-2c5543fc872d"
## [255] "21ddcc5f-163a-4ac6-91ec-80767391dad5"
## [256] "f7b5a7c7-0da2-49ec-8802-e1f94347f2f1"
## [257] "ccefbfdd-ed91-4d24-a7d8-729b1c73ad7f"
## [258] "31af0884-dae0-4eff-9ecf-94246de3712d"
## [259] "fafc578b-fdc2-453a-8e77-c74dd0159707"
## [260] "a62f075a-2d8b-4947-81da-5fac55ccbadc"
## [261] "250f57c1-3233-4a44-9f20-6ef89bd11f18"
## [262] "5e4d98f1-a6a8-4a9b-b99f-74e36cde9507"
## [263] "37a646b5-44b2-42c0-b951-55b57fa96492"
## [264] "559e209a-9a10-454a-830f-6d7515e7f016"
## [265] "96cfa366-2536-452a-8254-373fb1f855be"
## [266] "0b0ef362-ea74-47cd-becf-0e9170368ffb"
## [267] "b7a70788-d139-4313-87c0-329a109048a1"
## [268] "0c1f599d-ab06-4a33-9d24-9d0e01c72790"
## [269] "87cc19f3-220a-4550-88d7-aef9e386b28c"
## [270] "f503412d-ff16-4c81-9399-cd82841b54e8"
## [271] "e1f6780b-6e0e-47e9-a1ba-e3c5fb2e5ad9"
## [272] "e6987ef8-0307-4155-b7dd-136adf66d6fd"
## [273] "e0ad7809-a9b3-4c22-b9cd-15225c8d41b3"
## [274] "b84c335a-f2c2-4f3a-ac37-65ffde7d6f31"
## [275] "bf9e9ef1-e348-4e27-b5d4-77d220c26754"
## [276] "294c14f3-1786-4fd0-982e-b71e56e554c0"
## [277] "0815ad5a-9cb0-4f79-9147-dd6cbf3f3ac2"
## [278] "3240616c-5c7e-4636-96c0-067fed27d785"
## [279] "86eecea5-b185-4667-9975-7835acf9e8cd"
## [280] "c83907f6-5dfa-4392-aac1-daabacf8c99a"
## [281] "108ef905-7f95-43bd-acda-f73c178dc4f6"
## [282] "caf6aee8-d98c-4f57-ae6d-8c8e4f4d52a8"
## [283] "f1fa19f8-1648-4f19-8fae-88574fc7078d"
## [284] "acdb9666-f715-4d6c-817d-3e22b9019633"
## [285] "12f5985b-dece-4eea-b5bf-8bcbe888461f"
## [286] "593003ae-fb8d-4fcb-9010-c1b0280bca4f"
## [287] "830158aa-5c95-43cf-83d5-f0bf9a98177a"
## [288] "1de8e5a3-5ce6-4ef7-a0c6-9fac8d5abf71"
## [289] "39055688-a0eb-470c-bf23-c40b7ee86699"
## [290] "32231c36-8151-48f1-8865-d47fe432dd4a"
## [291] "dfc7b433-8ddf-4893-8601-3507154d130a"
## [292] "94e72574-86d4-417c-bf40-e3c51c44c79f"
## [293] "c9d4f4f8-6f12-4d2b-8a30-ebbd88b82ea3"
## [294] "d293e752-f897-4b9c-a9f4-ee8b8c6ba97d"
## [295] "ab41ada6-7f56-49bd-98c0-e62b19584059"
## [296] "a5e7eebf-16c8-42e8-9263-636c5af7c999"
## [297] "646e3a54-7715-4fe3-ad50-a4df9404812f"
## [298] "de81ad70-d0ef-4955-b25b-3bac3167dd13"
## [299] "7874ec30-bd5b-42b4-bfa4-f5c9d8af89e1"
## [300] "2d6c9cba-23cf-4564-ab0f-2545370ea463"
## [301] "c0571621-7eb6-4f90-b726-289ac3d618ed"
## [302] "022bdc87-7d48-423a-b09c-46405cd3857b"
## [303] "64ae8a0e-0de4-4d0d-b8ec-538c86fe3fe0"
## [304] "7ecca5ef-0896-481b-8728-20ae61f97207"
## [305] "1a04d465-d5df-4489-9d2f-80f3eadca921"
## [306] "b5496138-f4b0-4844-a11e-0727b77f2e44"
## [307] "acd2c494-18c5-4a6f-88a3-4469e9b18222"
## [308] "b8ff7bd6-dad2-43b7-a823-25eb1d0fe757"
## [309] "d395bd09-10ac-4b5c-bdfc-a07df57709b0"
## [310] "9e7ad40d-602b-4d18-aabe-27bf2b916fd5"
## [311] "27ee90d8-42cb-47cf-ac97-85eb1a8c3a30"
## [312] "67908989-f48f-48ba-9cdd-7877537e2644"
## [313] "a1c571cc-559f-4f67-b423-5799fc7d3424"
## [314] "fa7affb5-7980-45e9-89d3-0c6d21999f6e"
## [315] "3e860f1c-9ebc-4450-8e54-7e9bb33bdc87"
## [316] "f5fa5d79-e451-49e9-9360-5722ffa92dec"
## [317] "d15cc030-fcca-460a-8b07-c5c721ae1d1f"
## [318] "1b292d2e-0399-47bb-b1da-16546d9afb5e"
## [319] "c5748379-68d9-4124-a3f0-aac51c2dfaf9"
## [320] "f6c26fcc-2c5e-4aa1-83cd-11753826c62a"
## [321] "0edafb31-3ddb-41ca-bffe-b341abddae2f"
## [322] "e92fa84a-b812-49c9-aa35-f0e4ee8d6ba4"
## [323] "b2dce7ef-397b-42ac-8d7c-c02b2adae142"
## [324] "f7244e88-230e-497a-9df2-19361b053762"
## [325] "72fac31a-2fb8-4afd-be0e-80c3b210dfd4"
## [326] "e83bea01-419e-40c7-ba6f-ea964ecf11fd"
## [327] "55878eaa-e861-41eb-a5e7-7f126a618a3d"
## [328] "3e3ec9c2-51a3-4f81-bda6-65584872eaed"
## [329] "9eb3348f-4611-44bb-9642-c604991a2cbb"
## [330] "0318b5a2-aea0-4b5e-8fcc-b5f2b0c6e2fa"
## [331] "f9c8c06d-5f12-44d9-bb7f-b46b511b1af7"
## [332] "4d048c65-0bd9-4366-98d9-18f4144af45b"
## [333] "cf154c3f-966a-4598-97ef-dbcad2f003d5"
## [334] "945babac-0dc3-4081-a687-629c69b2441d"
## [335] "3e197aa8-ba84-452a-b1a4-c544cc37371d"
## [336] "cb3ef596-6e39-402f-875a-398e916a8192"
## [337] "71158d6e-31a0-4e2c-89e6-dcc04ba549b9"
## [338] "c2b9df8d-dc23-4468-8904-4a505c19741c"
## [339] "e882db4f-3405-4c2d-8bf6-668c0afa67a5"
## [340] "d5032e62-7459-4f66-8edf-e88cb59202e3"
## [341] "ce74df30-d12f-4921-a4cc-3e27f9ea4955"
## [342] "ddddaae4-9796-4bae-87f1-3e179658ef76"
## [343] "29c74c1e-2e6b-4ec1-9a57-e64cd541fbc1"
## [344] "fad35f39-4670-45ca-912e-8d706dafaa82"
## [345] "31c2a794-1199-4ba8-9a8f-48bd26fe7b14"
## [346] "ff5490e1-8879-469d-a5f9-f48d9211238b"
## [347] "06fd2d38-1904-4b05-be0f-9605334c11f7"
## [348] "8151c99f-8b5b-4166-a83d-1c86efc55ff4"
## [349] "795daed4-8669-4968-b2cf-dca99dd9dbab"
## [350] "8d7cb1c7-27e6-4314-925c-942628ad6e95"
## [351] "58dcbe29-2429-4246-bd1d-b7bdd54c264b"
## [352] "c40246c1-f71c-4ec5-9508-27f0f2d0b18e"
## [353] "5169894b-6e4a-4149-a780-16c3f51e888e"
## [354] "72a07440-630e-4483-81ba-0690ecc20ab4"
## [355] "4969bcd9-ede7-4e61-bce6-26491ae9bd57"
## [356] "abc8bd87-eea4-42d2-9e7b-e8935deef61d"
## [357] "3abb3e6c-45de-44bd-937e-eb0c8e9765b2"
## [358] "5e3ea80b-3e89-4356-a86c-8e7e67cf40b7"
## [359] "b1e7b0af-6941-42ae-be29-1b8d5cbbe1f1"
## [360] "2ecbb445-3752-407e-b7ef-b25f13537872"
## [361] "aab279b6-6a05-42da-9f21-8bf0f64549d1"
## [362] "9d2065a1-c011-4a6b-bbf1-c534fd794d4f"
## [363] "18ce5d23-fba1-46d0-832a-df1ee986f94c"
## [364] "cf0c821d-d897-4c50-9fe8-30549f2b058e"
## [365] "41bb46a3-a199-4613-a805-0a7b9b160b36"
## [366] "69c1e685-6d29-4b05-b70b-67feeec10d14"
## [367] "9faf4fbc-22f3-49af-a7e5-6ba39a2020d5"
## [368] "ef00a397-6ee3-424d-a62b-b57d7165bbc5"
## [369] "e8ae8056-cc87-4257-9b18-de2a6246d95d"
## [370] "8a84a4fb-9e99-472f-81ee-b34d5e8aaddc"
## [371] "69278dbc-b1a8-4ce5-a7f3-9008e3fc79a2"
## [372] "b8a6655b-6168-4d11-9879-961ef118fb0b"
## [373] "c6a37c32-27ff-4bb7-8767-8d75efc7fb94"
## [374] "fbc24b34-37f7-46d7-be8d-dd34b829b50a"
## [375] "c9c39ffe-02f6-491c-89fd-0644e231a359"
## [376] "66bc5a15-bff6-42df-bee4-d181c3f55cf7"
## [377] "d2f6196f-e835-497e-bccf-18edfe7c5a55"
## [378] "c6be523f-74b3-4416-a04d-5094fcd3f7a7"
## [379] "3257f846-da1e-4260-b536-24cabea0c023"
## [380] "9d746c6e-950a-4fda-b4b7-641f8da92fff"
## [381] "1549261e-97e0-4ae6-b8dc-56887ad8ec87"
## [382] "176013a5-ef33-4e7e-bb7a-1a89f06d4114"
## [383] "59d3580f-7a66-4419-84df-4abfa3133829"
## [384] "ca389774-fc21-4d13-945d-51e0a5c50297"
## [385] "8b76e6fd-88b7-40d5-9db3-f762ff52c82f"
## [386] "c88e5fd4-5d5f-42ad-b4de-5b2e835301c5"
## [387] "a09ba88b-57d6-42bc-99f8-e53037c665bf"
## [388] "bc803090-08b1-4a19-b3f7-193afc3785c5"
## [389] "49a47518-d35b-4321-9a29-6097af30b053"
## [390] "e7c017e4-1b5b-4352-a62e-c902080b4e95"
## [391] "07e4ae93-e91d-4849-bba6-2468474ff4c3"
## [392] "f26673c3-f2bb-41fa-a352-3a2878dad915"
## [393] "bb7097e8-189c-46aa-bdf3-ecdd8f19085d"
## [394] "a58f9f9e-c78c-4140-a31e-d6ba3890692e"
## [395] "8392dc86-02e9-4fbf-b5df-72bf1b39db73"
## [396] "25209eea-028b-4a57-be1c-d14345a0ed61"
## [397] "5afb7fc6-099e-4c3c-87cf-bb6fe067595a"
## [398] "ac1941c5-65d3-472f-920d-251180eae267"
## [399] "4a8c3e87-ecfe-4c87-a0d6-2ffb6e7b9506"
## [400] "c2716863-4368-43ba-9af5-4822dc234024"
## [401] "ee97e3b2-c9ea-42f1-87c4-fba10d62b175"
## [402] "32246824-b9d9-4b49-9635-cc89af1014c6"
## [403] "42da127b-46ef-4b61-ac53-df9b45aa2419"
## [404] "9853d444-a41f-4aa8-ac94-8d6566fc0f5e"
## [405] "ea25c04a-53b7-4cb2-97a3-cb8a75e0d982"
## [406] "12eb164e-b916-4871-9657-db06d988629e"
## [407] "6cef4db3-ab6b-439f-849a-9b15f2b9d697"
## [408] "eb8e81c7-b970-4337-87e6-5ae367bcc31e"
## [409] "90621308-9b54-4f36-ac86-1dac3d741f6f"
## [410] "4e4e585e-32aa-4575-84f7-107cc26d6b2e"
## [411] "0b43ccfc-6b2d-4457-991e-f1d56050f19d"
## [412] "010604ad-e6b1-4358-a5f0-371469ea5069"
## [413] "66b3cd18-1f5e-4f9b-b757-82276cdad37c"
## [414] "f130d7ae-77a2-4c40-95f8-ae9e2348987b"
## [415] "bc678ebc-fac4-4d9d-ab1a-740d548abcd5"
## [416] "932b55a2-fd24-4019-bfca-9180db0a5afc"
## [417] "8e906b02-e60e-470e-826d-f66e1996fd55"
## [418] "9cd6f2f4-191a-4fd3-a5c0-d8cef7dc9241"
## [419] "badea590-a44e-4fb2-a4d8-a5bc231cf64d"
## [420] "5f2dfca2-faae-4899-800b-94380aaaadd2"
## [421] "fe024e3d-db16-4e6e-a360-37888c5ba622"
## [422] "e6811a2c-5f56-48fb-8c68-d443a808d45e"
## [423] "4313e540-8957-49fa-962d-d3e9c9904b12"
## [424] "320f7be3-8b3d-4571-bb58-4011cb9629ba"
## [425] "eb333e5e-bdaa-4116-9640-cc8d2a7d72a5"
## [426] "0a278b12-de75-4c0a-8fb0-04d9368e306c"
## [427] "906bff07-4952-48d3-b4e9-7b825835ca49"
## [428] "4c9f62fc-01ae-4e32-baa9-eac30c536601"
## [429] "9aa333aa-cb6a-4c49-86d2-27edd05adc1e"
## [430] "302ecc56-e8b6-404a-9b8e-ebd10b9b9186"
## [431] "056dcea8-6daf-4fc6-9ae8-88207c3dcd8a"
## [432] "4ce4766f-3af9-4dff-89ac-5acb402479ab"
## [433] "a1ade888-3781-46bc-bc83-d3f5aa7dd16b"
## [434] "8f7b71bd-b307-4949-b95c-edceff0ff7ee"
## [435] "ab893325-afcd-4017-bd93-a307413c5cf3"
## [436] "6f59b20d-37a2-4f45-bc33-fd193c171e3c"
## [437] "2c1b917d-7b85-4c2e-8388-3c3b23f2f1a1"
## [438] "8ecb7219-b926-46c2-b0fe-b95d0154ae1c"
## [439] "b584c4e4-b8af-4f83-9883-67400d571505"
## [440] "13769705-13cc-48fe-a571-4855f47be61a"
## [441] "446934f5-c5cb-4216-ba77-2b4cde9daa60"
## [442] "1d1624e5-833b-4c0e-9fe1-189de5df3d34"
## [443] "0a54ed10-c3b3-4698-9432-07958f53b207"
## [444] "d4fba1d8-8b34-423d-8223-4d9686bcf1dc"
## [445] "44886096-9d58-4196-83f5-bfb63494d7a5"
## [446] "494c9dbe-71c0-4117-8647-8bd99d22b8df"
## [447] "38d0da92-4e66-4e4b-b918-8c96170777f4"
## [448] "560a662c-d9cd-45a7-8ab1-8d9a5cbd140e"
## [449] "9a9880ca-3bba-4be9-aeb4-fc19068cbe50"
## [450] "26f2594b-cbb7-4afb-ab41-64d11302759d"
## [451] "4cfe9246-a5e2-4b29-9003-37f92b92e8b7"
## [452] "29966089-2692-4530-9f47-6fb83f55a1f7"
## [453] "ce9a3eb4-47eb-4c70-8b7c-742abc673c65"
## [454] "b7fd7ab8-05bd-4270-847e-e7ffc580186d"
## [455] "38456a8e-a44f-4bf5-bfce-7eb39643385d"
## [456] "b11e92a1-0198-456a-b971-a2c6dac6a896"
## [457] "9ba7a58a-5e6f-42ae-9a15-199b5d62c4fe"
## [458] "7f37f43a-6457-43dd-925f-667bec41bf82"
## [459] "61b0abce-07be-4506-b0f5-49de8d8d9cb0"
## [460] "9bc26ec6-67e0-47b1-ab40-e817a95d8818"
## [461] "8a7dec69-49fc-42d0-8b58-fcf22a9e9932"
## [462] "a6dc42c2-3900-4651-a75a-13e33174e281"
## [463] "03225d48-d8f3-4795-a8f5-d0c5f4e2b483"
## [464] "b58f26c1-5e02-445b-8b75-ecf886e49199"
## [465] "27ec81a6-20e4-4333-ad48-0ac4d0edc79f"
## [466] "1843548b-e1d7-40e2-8212-f74ae8a71486"
## [467] "4e6e177a-126e-4c71-833c-c86d71f445b8"
## [468] "ebf4cb03-60a0-40e1-afc3-976252e62719"
## [469] "dc37f1ec-0366-40b3-a773-d0d1ef9630fd"
## [470] "f804dba3-3ee1-4639-a3e5-68da7c5c395a"
## [471] "3cab1bfb-2d6b-4a53-a18a-73f39d62969f"
## [472] "ba9b45ee-cd4e-46f2-98c1-c21483a13dac"
## [473] "1ed86869-6179-4b07-a3cb-8e10da440e98"
## [474] "82620fa0-ce02-4d1d-844f-6e91011a77ea"
## [475] "3afc6b3c-cd13-4a81-9f3a-413c6301e71d"
## [476] "7c0d69e9-ccd4-4017-84a3-6c4dccad694b"
## [477] "74ff6f92-7f9d-4c3f-822d-38c412d55dc3"
## [478] "1d3303e3-c0ef-4fcb-9d56-1661ccc78b76"
## [479] "e16dacc6-913a-432e-89ce-7b994c0d1bf2"
## [480] "7717a14b-b916-436e-913c-48e16e8f1216"
## [481] "e105d42b-da54-40eb-8851-4ba686f4a1c6"
## [482] "6ad0adcd-aedb-4a6c-84da-fb39932fd851"
## [483] "cf0fff8d-e717-49a6-8eb0-e65aaa231381"
## [484] "838be64d-a849-4d27-8aec-bca0a473e695"
## [485] "2bd27d69-ee8e-44c9-81a6-892b4f8465e1"
## [486] "7ba24396-201c-440b-86c5-9605b1706917"
## [487] "15c817eb-b169-4795-91de-8cf8539fb49a"
## [488] "736bcb6f-e62a-4a21-9ee5-60dbc03e6deb"
## [489] "05e7431c-6cd2-4980-9756-9b07fe4eae71"
## [490] "b4c5f573-223f-4150-a8cc-237e4d9e290b"
## [491] "6b6391d5-7ef3-4a74-aa2f-458c0a6a4f85"
## [492] "2c0c4120-bb8b-4b8f-8359-d1ffb244818e"
## [493] "b5291134-5055-45d8-9130-225d862feb20"
## [494] "2164a8a1-c467-4157-abee-c9bf146004e9"
## [495] "c2b2199f-1843-4502-a000-f20cf19bddb0"
## [496] "fcc5b0a6-34d9-4515-b81e-2c4ff17a5086"
## [497] "689d9753-f707-41ee-9d7f-ceaeb1b035d6"
## [498] "0b5e6e75-802a-49cf-ab71-935d65eacbbc"
## [499] "e2d0a861-6dd9-487d-9fc5-c13f33e0bd82"
## [500] "b0170e94-2026-4e8f-9ec5-f7c804152130"
## [501] "a8b3b5c3-cdba-4eb9-9444-c2f949507efd"
## [502] "219c0dc7-bba6-4f52-b009-f6cff46c8814"
## [503] "90b1a5e8-f411-4b39-a2f4-4c2c574b02ec"
## [504] "db9e46b7-a6f8-4db7-925b-7efb18b472a3"
## [505] "55003328-8b1e-459c-946b-8faf90504fdc"
## [506] "1d6c1eed-e6ca-477d-8192-4f629dd9d6ba"
## [507] "7b4f0ef6-a9ce-4177-acc5-38f8f93e85f6"
## [508] "6f8c1a2d-ad25-4b95-9024-6e16347f00c4"
## [509] "ea0efc32-7c3c-43ab-a007-60656073f9e8"
## [510] "8916c000-9fcc-4d87-b751-f3027b0e2082"
## [511] "963d146f-2960-4b02-9e0a-66cd09ee1605"
## [512] "2d6e53d9-9e17-48f2-97e7-154acdb996b7"
## [513] "a6097554-fca2-4853-b853-b159d9a8f572"
## [514] "3aaf5759-78cb-4596-b154-f9b5a2bb2726"
## [515] "3ea94763-bc87-4097-9cda-c08dafd8a8d5"
## [516] "d4075a1b-b12e-4b2d-9236-e118140be800"
## [517] "84fcf057-7410-4702-831d-2e88b9eaf616"
## [518] "6015e442-444a-487e-abd1-de3809560d3a"
## [519] "9f2d457e-3b18-4832-bfd0-a5cfa73aae44"
## [520] "14d0efdc-f518-4a9d-9b1d-b7dbbccf92ad"
## [521] "47e535fc-3b3a-4acf-974c-3956415bb101"
## [522] "74a1293a-29ab-418e-bded-fcb377b4aa6b"
## [523] "9f974332-a63f-4b58-8bfe-40b941a79e1e"
## [524] "0fa1a18c-2afe-459c-b7d8-c05461c79c8a"
## [525] "e44c6002-f73e-4a1a-a19e-1a0de9cbc742"
## [526] "4ec3713a-2424-490b-a46c-1f8df8ead736"
## [527] "7f73cb5d-6bb4-49e9-92af-8d9ce97bc66b"
## [528] "c8055d9c-94b8-43b4-bd1d-7aad4b97392f"
## [529] "d8450d57-dece-448a-88e1-d32a7d054b2e"
## [530] "e3006c17-4e4c-454b-b38a-2f0ea59a43ef"
## [531] "9e48abb1-3331-48d2-90ef-369091ac3963"
## [532] "26f31e18-1991-40eb-85fc-c9aa406b135b"
## [533] "cb540c5d-54b1-4dc3-add1-8b271c11352e"
## [534] "56351b3c-5897-421d-854f-ea77692b1234"
## [535] "bf6d3850-4cac-4073-b255-7e9ae8e43773"
## [536] "8d47a28e-dd61-4830-8d26-fd1b683d4acc"
## [537] "79a3b463-4766-4521-8b70-524aad54db8b"
## [538] "cf039ad6-7b2c-4732-bdc1-528060d45815"
## [539] "60873cd7-d373-459b-9642-a5dfe91b0fbc"
## [540] "d2376462-b4d6-4d6b-a0e9-d86f93629cf9"
## [541] "6addf87b-c43b-42be-8a76-44efa17bc0a2"
## [542] "d9562416-80e6-4aad-84bb-48c1d0d8f30a"
## [543] "f30fbc5c-4743-483d-857b-71c3162b602d"
## [544] "ce032981-672b-438e-a0b8-ac3ed8cbb98f"
## [545] "ba8077a4-2848-4210-9db6-4681bc043a5d"
## [546] "07a3fca4-5d74-4994-b269-842c5ab5bbaf"
## [547] "29642b15-19bf-47f9-b462-5701ec2f4c31"
## [548] "f75d6632-7114-409f-834b-9b9f8d2a6a39"
## 
## $Encounter.verbatimLocality
##  [1] "praia_grande"     NA                 "anjos"            "forno"           
##  [5] "prainha"          "anequim"          "pontal"           "pedra_vermelha"  
##  [9] "cardeiro"         "porcos_saltador"  "porcos_ponta_sul" "gracainha"       
## [13] "ilha_farol"       "enseada_abobora"  "porcos"           "ponta_leste"     
## [17] "boqueirao"       
## 
## $Encounter.year
##  [1] "2021" "2020" "2019" "2018" "2017" "2016" "2015" "2014" "2013" "2012"
## [11] "2010" "2009" "2008" "2007" "2006"
## 
## $Encounter.month
##  [1] "9"  "8"  "6"  "4"  "3"  "11" "10" "5"  "2"  "1"  "12" "7" 
## 
## $Encounter.day
##  [1] "30" "0"  "17" "18" "26" "25" "5"  "31" "20" "19" "9"  "23" "13" "14" "3" 
## [16] "16" "10" "21" "1"  "29" "15" "12" "11" "4"  "28" "27" "7"  "6"  "2"  "8" 
## [31] "24" "22"
## 
## $Encounter.behavior
## [1] NA         "resting"  "foraging"
## 
## $Encounter.genus
## [1] "Chelonia"     "Eretmochelys"
## 
## $Encounter.specificEpithet
## [1] "mydas"     "imbricata"
## 
## $Encounter.occurrenceRemarks
## [1] "left"  "right" "both" 
## 
## $Encounter.mediaAsset0
##   [1] "744.jpeg" "745.jpeg" "7.jpg"    "741.jpeg" "743.jpeg" "742.jpeg"
##   [7] "740.jpeg" "696.jpeg" "746.jpeg" "747.jpeg" "691.jpg"  "693.jpg" 
##  [13] "690.jpg"  "694.jpg"  "681.jpg"  "682.jpg"  "676.jpg"  "675.jpg" 
##  [19] "687.jpg"  "686.jpg"  "685.jpg"  "678.jpg"  "679.jpg"  "677.jpg" 
##  [25] "674.jpg"  "673.jpg"  "672.jpg"  "669.jpg"  "671.jpg"  "670.jpg" 
##  [31] "668.jpg"  "667.jpg"  "666.jpg"  "664.JPG"  "665.JPG"  "712.JPG" 
##  [37] "716.JPG"  "711.JPG"  "710.JPG"  "713.JPG"  "714.JPG"  "715.JPG" 
##  [43] "680.JPG"  "703.JPG"  "709.JPG"  "708.JPG"  "705.JPG"  "706.JPG" 
##  [49] "704.JPG"  "702.JPG"  "728.JPG"  "721.JPG"  "719.JPG"  "718.JPG" 
##  [55] "737.JPG"  "738.JPG"  "739.JPG"  "736.JPG"  "733.JPG"  "717.JPG" 
##  [61] "720.JPG"  "735.JPG"  "726.JPG"  "722.JPG"  "727.JPG"  "731.JPG" 
##  [67] "723.JPG"  "725.JPG"  "729.JPG"  "689.JPG"  "688.JPG"  "683.JPG" 
##  [73] "684.JPG"  "663.JPG"  "660.JPG"  "700.JPG"  "698.JPG"  "697.JPG" 
##  [79] "661.JPG"  "662.JPG"  "701.JPG"  "655.JPG"  "654.JPG"  "653.JPG" 
##  [85] "656.JPG"  "649.JPG"  "650.JPG"  "651.JPG"  "652.jpg"  "657.JPG" 
##  [91] "647.JPG"  "646.JPG"  "645.JPG"  "644.JPG"  "643.JPG"  "640.JPG" 
##  [97] "639.JPG"  "641.JPG"  "637.jpg"  "635.jpg"  "630.jpeg" "629.jpeg"
## [103] "615.jpeg" "612.jpg"  "610.jpg"  "609.jpg"  "611.jpg"  "613.jpeg"
## [109] "625.jpg"  "626.jpg"  "621.jpg"  "617.jpg"  "619.jpg"  "622.jpg" 
## [115] "620.jpg"  "627.jpg"  "624.jpg"  "623.jpg"  "634.jpg"  "631.jpg" 
## [121] "600.jpg"  "632.jpg"  "582.jpeg" "584.jpg"  "583.jpg"  "597.jpeg"
## [127] "575.jpeg" "580.jpeg" "577.jpeg" "596.jpg"  "492.jpg"  "491.jpg" 
## [133] "495.jpg"  "490.jpg"  "539.jpg"  "489.jpg"  "513.jpg"  "462.jpg" 
## [139] "482.jpg"  "585.jpg"  "481.jpg"  "529.jpeg" "531.jpg"  "511.jpg" 
## [145] "512.jpg"  "537.jpg"  "540.jpg"  "352.JPG"  "365.jpg"  "348.jpeg"
## [151] "338.JPG"  "532.jpg"  "340.JPG"  "339.JPG"  "346.jpeg" "541.jpg" 
## [157] "364.jpg"  "530.jpg"  "376.jpg"  "359.jpg"  "278.jpg"  "363.jpg" 
## [163] "159.jpg"  "266.jpg"  "592.jpg"  "391.jpg"  "240.jpg"  "285.jpg" 
## [169] "173.jpg"  "228.jpg"  "245.jpg"  "608.jpg"  "242.jpg"  "231.jpg" 
## [175] "155.jpg"  "374.jpg"  "293.jpg"  "360.jpg"  "414.jpg"  "460.jpg" 
## [181] "276.jpg"  "312.jpg"  "252.jpg"  "251.jpg"  "141.jpeg" "136.jpeg"
## [187] "151.jpg"  "604.jpg"  "152.jpg"  "153.jpg"  "256.jpg"  "255.jpg" 
## [193] "361.jpg"  "366.jpg"  "595.jpg"  "401.jpg"  "400.jpg"  "399.jpg" 
## [199] "479.jpg"  "275.jpg"  "263.jpg"  "147.jpg"  "154.jpg"  "239.jpg" 
## [205] "477.jpg"  "133.JPG"  "274.jpg"  "594.jpg"  "257.jpg"  "207.jpg" 
## [211] "254.jpg"  "140.jpg"  "134.jpeg" "341.jpg"  "204.jpg"  "132.JPG" 
## [217] "379.jpg"  "235.jpg"  "138.jpg"  "517.jpg"  "343.jpg"  "139.jpg" 
## [223] "543.jpg"  "268.jpg"  "265.jpg"  "264.jpg"  "258.jpg"  "148.jpg" 
## [229] "249.jpg"  "163.jpg"  "237.jpg"  "190.jpg"  "171.jpg"  "412.jpg" 
## [235] "606.jpg"  "162.jpg"  "607.jpg"  "161.jpg"  "322.jpg"  "395.jpg" 
## [241] "357.jpg"  "358.jpg"  "164.jpg"  "565.jpg"  "458.jpg"  "187.jpg" 
## [247] "176.jpg"  "211.jpg"  "601.jpg"  "404.jpg"  "195.jpg"  "405.jpg" 
## [253] "82.JPG"   "91.jpg"   "419.jpg"  "383.jpg"  "84.jpeg"  "202.jpg" 
## [259] "536.jpg"  "402.jpg"  "238.jpg"  "318.jpg"  "381.jpg"  "382.jpg" 
## [265] "350.jpg"  "380.jpg"  "416.jpg"  "415.jpg"  "271.jpg"  "418.jpg" 
## [271] "313.jpg"  "506.jpg"  "542.jpg"  "502.jpg"  "410.jpg"  "297.jpg" 
## [277] "291.jpg"  "406.jpg"  "321.jpg"  "169.jpg"  "325.jpg"  "145.jpg" 
## [283] "306.jpg"  "80.jpeg"  "304.jpg"  "261.jpg"  "501.jpg"  "389.jpg" 
## [289] "290.jpg"  "284.jpg"  "563.jpg"  "199.jpg"  "548.jpg"  "453.jpg" 
## [295] "146.jpg"  "315.jpg"  "553.jpg"  "303.jpg"  "387.jpg"  "279.jpg" 
## [301] "371.jpg"  "250.jpg"  "323.jpg"  "510.jpg"  "61.jpg"   "311.jpg" 
## [307] "378.jpg"  "377.jpg"  "182.jpg"  "179.jpg"  "296.jpg"  "165.jpg" 
## [313] "484.jpg"  "485.jpg"  "487.jpg"  "373.jpg"  "559.jpg"  "175.jpg" 
## [319] "326.jpg"  "76.jpg"   "351.jpg"  "224.jpg"  "508.jpg"  "191.jpg" 
## [325] "638.jpg"  "505.jpg"  "413.jpg"  "347.jpg"  "180.jpg"  "500.jpg" 
## [331] "498.jpg"  "208.jpg"  "388.jpg"  "564.jpg"  "177.jpg"  "496.jpg" 
## [337] "160.jpg"  "198.jpg"  "344.jpg"  "247.jpg"  "558.jpg"  "72.JPG"  
## [343] "277.jpg"  "192.jpg"  "225.jpg"  "349.jpg"  "567.jpg"  "299.jpg" 
## [349] "298.jpg"  "469.jpg"  "214.jpg"  "475.jpg"  "451.jpg"  "470.jpg" 
## [355] "438.jpg"  "218.jpg"  "259.jpg"  "450.jpg"  "178.jpg"  "70.JPG"  
## [361] "557.jpg"  "464.jpg"  "440.jpg"  "69.jpg"   "158.jpeg" "203.jpg" 
## [367] "446.jpg"  "566.jpg"  "262.jpg"  "260.jpg"  "535.jpg"  "456.jpg" 
## [373] "213.jpg"  "283.jpg"  "454.jpg"  "463.jpg"  "447.jpg"  "472.jpg" 
## [379] "183.jpg"  "471.jpg"  "445.jpg"  "217.jpg"  "216.jpg"  "200.jpg" 
## [385] "441.jpg"  "562.jpg"  "466.jpg"  "408.jpg"  "174.jpg"  "633.jpg" 
## [391] "186.jpg"  "307.jpg"  "222.jpg"  "335.jpg"  "221.jpg"  "227.jpg" 
## [397] "63.jpg"   "229.jpg"  "181.jpg"  "356.jpg"  "324.jpg"  "337.jpg" 
## [403] "561.jpg"  "386.jpg"  "129.jpg"  "280.jpg"  "568.jpg"  "572.jpg" 
## [409] "547.jpg"  "309.jpg"  "302.jpg"  "334.jpg"  "571.jpg"  "398.jpg" 
## [415] "555.jpg"  "62.jpg"   "573.jpg"  "320.jpg"  "193.jpg"  "184.jpg" 
## [421] "166.jpg"  "519.jpg"  "59.jpg"   "58.jpg"   "527.jpg"  "57.JPG"  
## [427] "522.jpg"  "189.jpg"  "234.jpg"  "427.jpg"  "556.jpg"  "429.jpg" 
## [433] "423.jpg"  "385.jpg"  "167.jpg"  "435.jpg"  "232.jpg"  "443.jpg" 
## [439] "436.jpg"  "425.jpg"  "422.jpg"  "196.jpg"  "538.jpg"  "420.jpg" 
## [445] "526.jpg"  "448.jpg"  "523.jpg"  "433.jpg"  "520.jpg"  "201.jpg" 
## [451] "220.jpg"  "437.jpg"  "514.jpg"  "528.jpg"  "525.jpg"  "370.jpg" 
## [457] "390.jpg"  "54.jpg"   "233.jpg"  "432.jpg"  "424.jpg"  "421.jpg" 
## [463] "215.jpg"  "586.jpg"  "331.jpg"  "587.jpg"  "210.jpg"  "434.jpg" 
## [469] "431.jpg"  "230.jpg"  "53.jpg"   "549.jpg"  "411.jpg"  "327.jpg" 
## [475] "51.jpg"   "397.jpg"  "544.jpg"  "367.jpg"  "394.jpg"  "409.jpg" 
## [481] "49.jpg"   "50.jpg"   "48.jpg"   "47.JPG"   "43.jpg"   "45.jpg"  
## [487] "44.jpg"   "368.jpg"  "332.jpg"  "476.jpg"  "392.jpg"  "42.jpg"  
## [493] "41.jpg"   "236.jpg"  "112.JPG"  "354.jpg"  "40.jpg"   "111.JPG" 
## [499] "336.jpg"  "126.jpg"  "110.JPG"  "39.jpg"   "38.JPG"   "109.JPG" 
## [505] "106.JPG"  "329.jpg"  "36.jpg"   "333.jpg"  "533.jpg"  "35.jpg"  
## [511] "105.jpg"  "122.jpg"  "123.jpg"  "103.JPG"  "104.JPG"  "31.JPG"  
## [517] "33.JPG"   "30.jpg"   "372.jpg"  "534.jpg"  "28.JPG"   "27.jpg"  
## [523] "25.jpg"   "98.JPG"   "101.JPG"  "102.JPG"  "117.JPG"  "24.JPG"  
## [529] "22.JPG"   "23.JPG"   "20.JPG"   "21.JPG"   "15.JPG"   "12.jpg"  
## [535] "97.jpg"   "11.jpg"   "9.jpg"    "10.jpg"   "8.jpg"    "6.jpg"   
## [541] "4.jpg"    "96.jpg"   "5.jpg"    "328.jpg"  "3.JPG"    "2.JPG"   
## [547] "1.JPG"   
## 
## $Encounter.mediaAsset1
##  [1] NA         "695.jpeg" "707.JPG"  "734.JPG"  "732.JPG"  "724.JPG" 
##  [7] "730.JPG"  "669.jpg"  "636.jpg"  "616.jpeg" "614.jpeg" "628.jpg" 
## [13] "599.jpeg" "598.jpeg" "576.jpeg" "578.jpeg" "494.jpg"  "480.jpg" 
## [19] "345.jpeg" "267.jpg"  "172.jpg"  "384.jpg"  "142.jpeg" "273.jpg" 
## [25] "593.jpg"  "135.jpeg" "342.jpg"  "396.jpg"  "602.jpg"  "92.JPG"  
## [31] "403.jpg"  "272.jpg"  "417.jpg"  "507.jpg"  "503.jpg"  "292.jpg" 
## [37] "168.jpg"  "316.jpg"  "483.jpg"  "486.jpg"  "488.jpg"  "509.jpg" 
## [43] "473.jpg"  "499.jpg"  "497.jpg"  "439.jpg"  "157.jpeg" "185.jpg" 
## [49] "301.jpg"  "426.jpg"  "552.jpg"  "393.jpg"  "99.JPG"

Configurando a data. Em caso de ausência do dia, foi padronizado uso do dia 15

originalenctable$Encounter.day[originalenctable$Encounter.day==0] <- 15
tabledate <- unite(originalenctable, Encounter.year, Encounter.month, Encounter.day, col = "Date", sep = "-")
tabledate$Date <- factor(tabledate$Date)
tabledate$Date <- as.Date(tabledate$Date, format = "%Y-%m-%d")

Definindo qual(is) lateral(is) os indivíduos têm ao longo do banco de dados

tablesides <- tabledate %>%
  filter(!is.na(Name0.value)) %>%
  group_by(Name0.value) %>% 
  mutate(Sides = case_when(any(str_detect(Encounter.occurrenceRemarks,"both")|n_distinct(Encounter.occurrenceRemarks) > 1) ~ 'both',
                           TRUE ~ Encounter.occurrenceRemarks )) %>%
  ungroup

Definindo quais indivíduos já foram revistos

tableresight <- tablesides %>%
  filter(!is.na(Name0.value)) %>%
  group_by(Name0.value) %>% 
  mutate(Resight = case_when (any(n_distinct(Occurrence.occurrenceID) > 1) ~ 'yes',
                           TRUE ~ 'no' ))

Tabela final com o número total de indivíduos

tableturtlesmax <- tableresight %>% 
  filter(!is.na(Name0.value))

Número de fotos que foram avaliadas para criar esse banco de dados

tableturtlesmax %>%
  filter(!is.na(Encounter.mediaAsset0)) %>%
  group_by(Encounter.genus) %>% 
  dplyr::summarise(n_distinct (Encounter.mediaAsset0))

## # A tibble: 2 x 2
##   Encounter.genus `n_distinct(Encounter.mediaAsset0)`
##   <chr>                                         <int>
## 1 Chelonia                                        438
## 2 Eretmochelys                                    109

tableturtlesmax %>%
  filter(!is.na(Encounter.mediaAsset1)) %>%
  group_by(Encounter.genus) %>% 
  dplyr::summarise(n_distinct (Encounter.mediaAsset1))

## # A tibble: 2 x 2
##   Encounter.genus `n_distinct(Encounter.mediaAsset1)`
##   <chr>                                         <int>
## 1 Chelonia                                         41
## 2 Eretmochelys                                     11

Contando o número de indivíduos distintos para cada espécie, de acordo com a(s) lateral(is) que os indivíduos foram registrados.

tableturtlesmax %>%
  filter(!is.na(Name0.value)) %>%
  filter(Encounter.genus == "Chelonia") %>%
  distinct (Name0.value, .keep_all= TRUE) %>% 
  group_by(Sides) %>% 
  dplyr::summarise(count = n())

## # A tibble: 3 x 2
##   Sides count
##   <chr> <int>
## 1 both     30
## 2 left    126
## 3 right   130

tableturtlesmax %>%
  filter(!is.na(Name0.value)) %>%
  filter(Encounter.genus == "Eretmochelys") %>%
  distinct (Name0.value, .keep_all= TRUE) %>% 
  group_by(Sides) %>% 
  dplyr::summarise(count = n())

## # A tibble: 3 x 2
##   Sides count
##   <chr> <int>
## 1 both      9
## 2 left     22
## 3 right    14

Para evitar possível replicação de indivíduos, é preciso selecionar o número mínimo de indivíduos confirmados. Esse número é o valor dos indivíduos identificados que possuem as duas laterais ‘both’ somado com a lateral que mais possui indivíduos identificados. Nesse caso ‘right’ para C. mydas e ‘left’ para E. imbricata.

tableturtlesmin <-tableturtlesmax %>%
  filter(!is.na(Name0.value)) %>%
  filter(Sides %in% c('both', 'right') & Encounter.genus == "Chelonia" | 
         Sides %in% c('both', 'left') & Encounter.genus == "Eretmochelys")

Analisando o número mínimo de indivíduos confirmados

Número de fotos por ano, para cada espécie

tableturtlesmin %>% 
  distinct (Name0.value, .keep_all= TRUE) %>%
  mutate(year = format(Date, "%Y")) %>%
  group_by(year) %>%
ggplot(., aes(x=year, fill=Encounter.genus))+
  geom_bar(width=.8, position = position_dodge2(preserve = "single")) +
  scale_x_discrete(name = "Ano", breaks = seq(2006, 2021, 1)) +
  scale_y_continuous(name = "Fotos", breaks = seq(0, 100, 20),expand = c(0,0,0,1))

Número de Indivíduos por ano, para cada espécie

tableturtlesmin %>% 
  distinct (Name0.value, .keep_all= TRUE) %>%
  mutate(year = format(Date, "%Y")) %>%
  group_by(year) %>%
ggplot(., aes(x=year, fill= Encounter.genus))+
  geom_bar() +
  scale_y_continuous(name = "Indivíduos", breaks = seq(0, 110, 10), expand = c(0,0,0,15))+
  scale_x_discrete(name = "Ano")+
  theme(legend.position = "none")+
  facet_grid(rows = vars(Encounter.genus),scales = "free", space = "free")

Quantidade e frequência de indivíduos que foram revistos

tableturtlesmin %>%
  filter(!is.na(Encounter.mediaAsset0)) %>%
  distinct (Name0.value, .keep_all= TRUE) %>% 
  group_by(Encounter.genus, Resight) %>% 
  dplyr::summarise(count = n()) %>% 
  ungroup() %>% 
  mutate(freq = count / sum(count))

## `summarise()` has grouped output by 'Encounter.genus'. You can override using the `.groups` argument.

## # A tibble: 4 x 4
##   Encounter.genus Resight count   freq
##   <chr>           <chr>   <int>  <dbl>
## 1 Chelonia        no        116 0.607 
## 2 Chelonia        yes        44 0.230 
## 3 Eretmochelys    no         24 0.126 
## 4 Eretmochelys    yes         7 0.0366

Presença dos indivíduos revistos nas fotos

tableturtlesmin %>%
  filter(!is.na(Encounter.mediaAsset0)) %>%
  group_by(Encounter.genus, Resight) %>% 
  dplyr::summarise(n_distinct (Encounter.mediaAsset0))

## `summarise()` has grouped output by 'Encounter.genus'. You can override using the `.groups` argument.

## # A tibble: 4 x 3
## # Groups:   Encounter.genus [2]
##   Encounter.genus Resight `n_distinct(Encounter.mediaAsset0)`
##   <chr>           <chr>                                 <int>
## 1 Chelonia        no                                      116
## 2 Chelonia        yes                                     168
## 3 Eretmochelys    no                                       24
## 4 Eretmochelys    yes                                      65

tableturtlesmin %>%
  filter(!is.na(Encounter.mediaAsset1)) %>%
  group_by(Encounter.genus, Resight) %>% 
  dplyr::summarise(n_distinct (Encounter.mediaAsset1))

## `summarise()` has grouped output by 'Encounter.genus'. You can override using the `.groups` argument.

## # A tibble: 4 x 3
## # Groups:   Encounter.genus [2]
##   Encounter.genus Resight `n_distinct(Encounter.mediaAsset1)`
##   <chr>           <chr>                                 <int>
## 1 Chelonia        no                                        8
## 2 Chelonia        yes                                      32
## 3 Eretmochelys    no                                        4
## 4 Eretmochelys    yes                                       7

Análise dos indivíduos revistos

Histograma

Filtrando apenas os indivíduos que foram revistos

turtlesresighted <- tableturtlesmin %>%
  filter(Resight == "yes")

Identificando a data dos encontros

table_sum <- turtlesresighted %>%
  group_by(Date, Name0.value, Encounter.genus) %>%
  dplyr::summarise(count = n()) %>% 
  mutate_at(vars(count), funs(factor))

## `summarise()` has grouped output by 'Date', 'Name0.value'. You can override using the `.groups` argument.

## Warning: `funs()` was deprecated in dplyr 0.8.0.
## Please use a list of either functions or lambdas: 
## 
##   # Simple named list: 
##   list(mean = mean, median = median)
## 
##   # Auto named with `tibble::lst()`: 
##   tibble::lst(mean, median)
## 
##   # Using lambdas
##   list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

Calculando o intervalo entre o primeiro e último encontro dos indivíduos.

table_range <- table_sum %>%
  group_by(Name0.value, Encounter.genus) %>%
  dplyr::summarise(min = min(Date),
                   max = max(Date)) %>% 
  ungroup() %>% 
  mutate(diff_dias=max-min) %>% 
  mutate(diff_meses=diff_dias/(365.25/12)) %>%
  mutate(diff_anos=diff_dias/(365)) %>%
  mutate_at(vars(diff_meses,diff_anos), round, 1) %>%
  mutate_if(is.difftime,as.numeric)

## `summarise()` has grouped output by 'Name0.value'. You can override using the `.groups` argument.

Calculando descritores do intervalo entre o primeiro e último encontro de cada indivíduo

describeBy(table_range$diff_anos, table_range$Encounter.genus)

## 
##  Descriptive statistics by group 
## group: Chelonia
##    vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 44 1.73 1.52   1.65    1.58 1.85   0 5.4   5.4 0.66    -0.57 0.23
## ------------------------------------------------------------ 
## group: Eretmochelys
##    vars n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 7 2.66 2.07    2.4    2.66 1.78 0.1 5.6   5.5 0.32     -1.7 0.78

median <- ddply(table_range, .(Encounter.genus), summarise, median = median(diff_anos))

Histograma do intervalo entre os encontros com a mediana

ggplot(table_range, aes(x=diff_anos)) + 
  geom_histogram(binwidth= 1, alpha=.7, boundary = 0, aes(fill=Encounter.genus, color=Encounter.genus))+
  scale_y_continuous(name = "Indivíduos",breaks = seq(0, 22, 2), expand = c(0,0,0,2))+
  scale_x_continuous(name = "Intervalo (anos)",breaks = 1:6)+
  geom_vline(data=median, aes(xintercept=median),
             linetype="dashed")+
  facet_grid (rows = vars(Encounter.genus),scales = "free", space = "free")+
  theme(legend.position = "none")

Linha do tempo

Nessa etapa foi escolhido filtrar os indivíduos onde o intervalo entre o primeiro e último encontro é maior do que 12 meses.

table_range <- table_range %>%
  filter ( diff_meses > 12 )

Selecionando os indivíduos de ‘table_range’ em ‘table_sum’

table_sum_ok <- table_sum %>% 
  filter(Name0.value %in% table_range$Name0.value)

Linha do tempo dos indivíduos, marcando todos os encontros

ggplotly (ggplot(table_sum_ok, aes(x = Date, y = Name0.value)) +
  geom_segment(data = table_range, size = 1.6, alpha=.4,
               aes(x = min, xend = max, y = Name0.value, yend = Name0.value))+
  geom_point(aes(color=Encounter.genus), alpha=1, size=2.7) +
  labs(x="Tempo", y="Indivíduos")+
  scale_x_date(date_breaks = "3 months", date_labels = "%m/%Y")+
  theme_light()+
  theme(axis.text.x = element_text(angle = 45, hjust = .9, vjust = .9)))